Freischutz digital: A multimodal scenario for informed music processing

Müller, Meinard; Prätzlich, Thomas; Bohl, Benjamin W.; Veit, Joachim

doi:10.1109/wiamis.2013.6616168

Cited by 3 publications

(3 citation statements)

References 14 publications

(11 reference statements)

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“…However, we also see that 2 We want to thank Elias Kokkinis who has provided the code for his algorithm and a parameter setting specifically tuned for this recording. 3 The recording is an excerpt of the opera "Der Freischütz" recorded by the project Freischütz Digital (www.freischuetz-digital.de) [16]. the different versions of KAMIR perform favorably as well, especially when the model benefits from several microphones for one voice signal.…”

Section: Discussionmentioning

confidence: 99%

Kernel Additive Modeling for interference reduction in multi-channel music recordings

Prätzlich

Bittner

Müller

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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When recording a live musical performance, the different voices, such as the instrument groups or soloists of an orchestra, are typically recorded in the same room simultaneously, with at least one microphone assigned to each voice. However, it is difficult to acoustically shield the microphones. In practice, each one contains interference from every other voice. In this paper, we aim to reduce these interferences in multi-channel recordings to recover only the isolated voices. Following the recently proposed Kernel Additive Modeling framework, we present a method that iteratively estimates both the power spectral density of each voice and the corresponding strength in each microphone signal. With this information, we build an optimal Wiener filter, strongly reducing interferences. The trade-off between distortion and separation can be controlled by the user through the number of iterations of the algorithm. Furthermore, we present a computationally effective approximation of the iterative procedure. Listening tests demonstrate the effectiveness of the method.

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Section: Discussionmentioning

confidence: 99%

Kernel Additive Modeling for interference reduction in multi-channel music recordings

Prätzlich

Bittner

Müller

2015

2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

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“…Music is widely considered a universal language and there are many previous discussions on music representations (Dannenberg 1993;Wiggins, Müllensiefen, and Pearce 2010;Müller et al 2013). The relationship between music notation (score) and actual sound is similar to the one between text and speech.…”

Section: Multi-level and Multi-modal Representationmentioning

confidence: 99%

Music Style Transfer: A Position Paper

Dai¹,

Zhang²,

Xia³

2018

Preprint

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Led by the success of neural style transfer on visual arts, there has been a rising trend very recently in the effort of music style transfer. However, "music style" is not yet a well-defined concept from a scientific point of view. The difficulty lies in the intrinsic multi-level and multi-modal character of music representation (which is very different from image representation). As a result, depending on their interpretation of "music style", current studies under the category of "music style transfer", are actually solving completely different problems that belong to a variety of sub-fields of Computer Music. Also, a vanilla end-to-end approach, which aims at dealing with all levels of music representation at once by directly adopting the method of image style transfer, leads to poor results. Thus, we vitally propose a more scientifically-viable definition of music style transfer by breaking it down into precise concepts of timbre style transfer, performance style transfer and composition style transfer, as well as to connect different aspects of music style transfer with existing well-established sub-fields of computer music studies. In addition, we discuss the current limitations of music style modeling and its future directions by drawing spirit from some deep generative models, especially the ones using unsupervised learning and disentanglement techniques.

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“…As noted, music information can reach beyond isolated audio and thus can be more appropriately modeled as multimodal information streams, including combinations of audio, video, images, music scores, text, and gestures. [2][3][4][5][6] Analyzing, describing, and indexing this information presents considerable research challenges: frequently, information that is salient or meaningful to humans is not trivially extracted from the signals.…”

Section: Research Challengesmentioning

confidence: 99%